Most studies in this third group rely on predictive models trained on historical process execution data (event logs) to determine for which cases and when an intervention should be triggered. Fahrenkrog et al. [1]} propose a PrPM approach that relies on predictions obtained from an outcome-oriented predictive model [2]} to determine if an intervention should be triggered for an ongoing case of the process. Specifically, this and similar methods trigger an intervention when the probability of an undesired outcome (herein denoted \(\mathbb {P}(uout)\) ) exceeds a threshold. This threshold is determined via a so-called empirical thresholding mechanism, which effectively tries out multiple thresholds over a subset of the event log to find the threshold that maximizes a reward function. This technique does not take into account the uncertainty inherent in prediction models.
